Encapsulation of nanoparticles by polymerization compounding in a gas/solid fluidized bed reactor
Bibliographic record
Abstract
Abstract For the first time, a fluidized bed reactor was used for encapsulating nanoparticles by the polymerization compounding approach using Ziegler–Natta catalysts. The polymerization reaction was carried out using a solvent‐free process in a gas‐phase reactor. This direct gas–solid reaction greatly simplified collecting the particles of interest after polymerization because none of the extra steps often found in encapsulation processes, such as filtering and drying, were performed in this work. The grafting of the catalyst to the original surface of particles was confirmed by X‐ray photoelectron spectroscopy. Micrographs obtained by transmission electron microscopy confirmed the presence of a thin layer of polymer, in the order of a few nanometers, around the particles. The thickness of this coating was affected by the operating conditions of the process. The characterization of the modified particles with electron microscopy also revealed that zirconia nanoparticles tend to be coated in an agglomerated state, whereas aluminum particles were mostly individually encapsulated by the polymer. In addition, the effects of temperature and pressure were studied on the encapsulation process and a kinetic analysis was presented based on the available models in the literature. © 2009 American Institute of Chemical Engineers AIChE J, 2009
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".